What is it?
What it is
TokenSpeed is a speed-of-light LLM inference engine designed for agentic workloads, delivering TensorRT-LLM-level performance with vLLM-level usability.
Why it exists
To provide the most performant inference engine for production agentic workloads, overcoming the trade-off between raw speed and ease of use.
Who should use it
ML engineers, infrastructure engineers, and researchers building production-grade LLM serving systems for agentic workloads who need high-throughput inference.
Who should avoid it
Beginners unfamiliar with LLM serving concepts, users seeking a simple plug-and-play solution without performance tuning, and those not working on agentic or high-throughput scenarios.
How it works
A quick walkthrough in plain English
How tokenspeed works
Step 1 of 3
You interact with it
Open tokenspeed, send a request, or connect it to your stack.
Features
Advantages
- High inference throughput (up to 580 TPS on large models)
- Performance comparable to TensorRT-LLM
- Ease of use similar to vLLM
- Automatic parallelism via static compiler eliminates manual parallelism coding
- Safe KV cache resource reuse enforced at compile time
- Pluggable and extensible kernel system
- Low-overhead CPU request handling via AsyncLLM
- Active development with regular blogs and performance updates
- Broad model compatibility across multiple architectures
Disadvantages
- Optimal performance currently tied to Blackwell GPUs; may require specific hardware
- Newer engine may have fewer community integrations and third-party tooling compared to established solutions
- Learning curve associated with SPMD modeling and static compilation concepts
- Limited documentation beyond getting started guides; advanced customization may require deep dive into source
Installation
FAQ
What is TokenSpeed and what is its primary use case?
TokenSpeed is a speed-of-light LLM inference engine designed for agentic workloads, optimized for high performance in production scenarios involving complex agent interactions.
How does TokenSpeed achieve high performance for agentic workloads?
TokenSpeed uses a local-SPMD design with a static compiler to automate parallelism logic, combined with a pluggable kernel system featuring an optimized MLA (Multi-head Latent Attention) implementation for efficient inference.
What are the key components of TokenSpeed's architecture?
TokenSpeed's core components include a modeling layer with automatic parallelism, a C++/Python scheduler with a finite-state machine for request handling, a pluggable kernel system with an MLA implementation, and an SMG-integrated AsyncLLM entrypoint.
How can I get started with using TokenSpeed?
You can start by following the 'Getting Started' guide in the documentation, which includes steps for launching a server and configuring models for inference.
Which models are compatible with TokenSpeed?
TokenSpeed supports a wide range of models, including those from the topics listed in its metadata such as Qwen, Kimi, GLM, and others, with specific recipes available in the documentation for model deployment.
What performance benchmarks does TokenSpeed offer?
TokenSpeed has achieved up to 580 TPS on Qwen3.5-397B-A17B for agentic workloads, as demonstrated in its performance comparison benchmarks against TensorRT-LLM and vLLM.
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